A Rule Based Events Correlation Algorithm for Process Mining

Abstract

Process mining is a technique for extracting process models from event logs. Process mining can be used to discover, monitor and to improve real business processes by extracting knowledge from event logs available in process-aware information systems. This paper is concerned with the problem of grouping events in instances and the preparation of data for the process mining analysis. Often information systems do not store a unique identifier of the case instance, or errors happen in the system during the recording of events in the log files. To be able to analyze the process, it is necessary that events are grouped into case instances. The aim of the presented rule based algorithm is to find events belonging to the same case instance. Performances of the algorithm, for different sizes of log file events and different levels of errors within log files in the real process, have been analyzed.